October 2020 arXiv papers — page 108

Tianyi Liu, Ioannis Paradisanos, Jijun He, Alisson R. Cadore

Monolayer transition metal dichalcogenides with direct bandgaps are emerging candidates for microelectronics, nano-photonics, and optoelectronics. Transferred onto photonic integrated circuits (PICs), these semiconductor materials have enabled new classes of light-emitting diodes, modulators and photodetectors, that could be amenable to wafer-scale manufactu

Gisela Anton

This paper addresses the working principle of neutrino telescopes, important detector parameters as well as the layout and performance of current and future neutrino telescopes. It was prepared for the book "Probing Particle Physics with Neutrino Telescopes", C.~P{é}rez de los Heros, editor, 2020 (World Scientific) in 2018.

LHCb collaboration, R. Aaij, C. Abellán Beteta, T. Ackernley

An angular analysis of the $B^0 \to K^{*0} e^+ e^-$ decay is performed using a data sample corresponding to an integrated luminosity of $9~{\rm fb}^{-1}$ of $pp$ collisions collected with the LHCb experiment. The analysis is conducted in the very low dielectron mass squared ($q^2$) interval between $0.0008$ and $0.257~{\rm GeV}^2$, where the rate is dominate

Jiakun Liu, Stuart Leigh Phoenix

Stiffness degradation and progressive failure of composite laminates are complex processes involving evolution and multi-mode interactions among fiber fractures, intra-ply matrix cracks and inter-ply delaminations. This paper presents a novel finite element model capable of explicitly treating such discrete failures in laminates of random layup. Matching of

Brian Allen

In this article we reduce the geometric stability conjecture for the scalar torus rigidity theorem to the conformal case via the Yamabe problem. Then we are able to prove the case where a sequence of Riemannian manifolds is conformal to a uniformly controlled sequence of flat tori and satisfies the geometric stability conjecture. We are also able to handle t

Weiwei Ao, Azahara DelaTorre, Maria del Mar Gonzalez

In this paper, we will consider the fractional Caffarelli-Kohn-Nirenberg inequality \begin{equation*} Λ \left(\int_{\mathbb R^n}\frac{|u(x)|^{p}}{|x|^{β {p}}}\,dx\right)^{\frac{2}{p}}\leq \int_{\mathbb R^n}\int_{\mathbb R^n}\frac{(u(x)-u(y))^2}{|x-y|^{n+2γ}|x|^{α}|y|^{α}}\,dy\,dx \end{equation*} where $γ\in(0,1)$, $n\geq 2$, and $α,β\in\mathbb R$ satisfy \be

Lam Duc Nguyen, Israel Leyva-Mayorga, Amari N. Lewis, Petar Popovski

Mobile devices with embedded sensors for data collection and environmental sensing create a basis for a cost-effective approach for data trading. For example, these data can be related to pollution and gas emissions, which can be used to check the compliance with national and international regulations. The current approach for IoT data trading relies on a ce

Grady Booch, Francesco Fabiano, Lior Horesh, Kiran Kate

This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities

Xiaowei Wang

In this paper we provide a new series representation for the values of Riemann zeta function at integer arguments, namely: $ ζ(m)=\sum_{n=1}^{\infty}\frac{m(-1)^{n-1}Γ(1-ω_{m}n)...Γ(1-ω_{m}^{m-1}n)}{n!n^m}$, where $n$ is an integer that lager than $1$ and $ω$ is the $m$-th root of unity. This series converges quite fast. It's derived by some technique of

Suman K. Bera, Lior Gishboliner, Yevgeny Levanzov, C. Seshadhri

We consider the problem of counting the number of copies of a fixed graph $H$ within an input graph $G$. This is one of the most well-studied algorithmic graph problems, with many theoretical and practical applications. We focus on solving this problem when the input $G$ has bounded degeneracy. This is a rich family of graphs, containing all graphs without a

Xing Jie Zhong, David Chiang

Despite advances in neural machine translation (NMT) quality, rare words continue to be problematic. For humans, the solution to the rare-word problem has long been dictionaries, but dictionaries cannot be straightforwardly incorporated into NMT. In this paper, we describe a new method for "attaching" dictionary definitions to rare words so that the

Min Du, Nesime Tatbul, Brian Rivers, Akhilesh Kumar Gupta

Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task. Metrics such as Balanced Accuracy are commonly used to evaluate a classifier's prediction performance under such scenarios. However, these metrics fall short when classes vary in importanc

T. Phatak, K. B. Nakshatrala

Topology optimization (TopOpt) is a mathematical-driven design procedure to realize optimal material architectures. This procedure is often used to automate the design of devices involving flow through porous media, such as micro-fluidic devices. TopOpt offers material layouts that control the flow of fluids through porous materials, providing desired functi

Jordan J. Bird, Anikó Ekárt, Diego R. Faria

In this work, we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of deep learning chatbots for task classification. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transf

Dieter Bothe

We revisit the sharp-interface continuum thermodynamics of two-phase multicomponent fluid systems with interfacial mass. Since the published work is not fully consistent, we provide a rigorous derivation of the local balance equations and the entropy production rates, including all relevant steps and mathematical tools. Special emphasis is put on an axiomati

Vijay V. Vazirani

We prove that a fractional perfect matching in a non-bipartite graph can be written, in polynomial time, as a convex combination of perfect matchings. This extends the Birkhoff-von Neumann Theorem from bipartite to non-bipartite graphs. The algorithm of Birkhoff and von Neumann is greedy; it starts with the given fractional perfect matching and successively

Gaojie Jin, Xinping Yi, Liang Zhang, Lijun Zhang

This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability. For fully-connected layers, the weight correlation is defined as the average cosine similarity between weight vectors of neurons, and for convolutional layers, the weight correlation is defined as the cosine

Sahar Tavakoli

The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is a weighted graph that i

Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei

Shape and texture are two prominent and complementary cues for recognizing objects. Nonetheless, Convolutional Neural Networks are often biased towards either texture or shape, depending on the training dataset. Our ablation shows that such bias degenerates model performance. Motivated by this observation, we develop a simple algorithm for shape-texture debi

Adam Kapelner, Abba Krieger

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pa

Alex H. Barnett

We present a fast algorithm for computing the diffracted field from arbitrary binary (sharp-edged) planar apertures and occulters in the scalar Fresnel approximation, for up to moderately high Fresnel numbers ($\lesssim 10^3$). It uses a high-order areal quadrature over the aperture, then exploits a single 2D nonuniform fast Fourier transform (NUFFT) to eval

Bhaskar Dutta, Arpan Kar, Louis E. Strigari

We investigate the possibilities for probing MeV dark matter (DM) particles and primordial black holes (PBHs) (for masses $\sim 10^{15}$--$10^{17}$ g) at the upcoming radio telescope SKA, using photon signals from the Inverse Compton (IC) effect within a galactic halo. Pair-annihilation or decay of MeV DM particles (into $e^+ e^-$ pairs) or Hawking radiation

Pierre-Marie Boulvard, Peng Gao, Vahagn Nersesyan

We study the problems of controllability and ergodicity of the system of 3D primitive equations modeling large-scale oceanic and atmospheric motions. The system is driven by an additive force acting only on a finite number of Fourier modes in the temperature equation. We first show that the velocity and temperature components of the equations can be simultan

Laura Sberna, Alexandre Toubiana, M. Coleman Miller

We study the evolution and gravitational wave emission of white dwarf -- black hole accreting binaries with a semi-analytical model. These systems will evolve across the mHz gravitational wave frequency band and potentially be detected by the Laser Interferometer Space Antenna (LISA). We identify new universal relations for this class of binaries, which rela

Erick Nagel, Jerome Bouvier

The dipper optical light curves in young stellar objects are commonly interpreted as partial or total occultation of the stellar radiation by dust surrounding the star. In this work, we analyze the amplitude of the optical light curve of V715 Per, located in the young star forming region IC 348. Observations gathered over the years suggest that the light cur

Hannes Mueller, Andre Groger, Jonathan Hersh, Andrea Matranga

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent

Hang Zhu, Kostis Kaffes, Zixu Chen, Zhenming Liu

Low-latency online services have strict Service Level Objectives (SLOs) that require datacenter systems to support high throughput at microsecond-scale tail latency. Dataplane operating systems have been designed to scale up multi-core servers with minimal overhead for such SLOs. However, as application demands continue to increase, scaling up is not enough,

Francesco Parino, Lorenzo Zino, Maurizio Porfiri, Alessandro Rizzo

To date, the only effective means to respond to the spreading of COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reduce their social and economical consequences. Here, we introduce a meta-population model based on

Sara Saeidian, Giulia Cervia, Tobias J. Oechtering, Mikael Skoglund

Machine learning models are known to memorize the unique properties of individual data points in a training set. This memorization capability can be exploited by several types of attacks to infer information about the training data, most notably, membership inference attacks. In this paper, we propose an approach based on information leakage for guaranteeing

Markian Hromiak

Taking the stance that artificially conscious agents should be given human-like rights, in this paper we attempt to define consciousness, aggregate existing universal human rights, analyze robotic laws with roots in both reality and science fiction, and synthesize everything to create a new robot-ethical charter. By restricting the problem-space of possible

Jukka I. Väyrynen, Dmitry I. Pikulin, Roman M. Lutchyn

We investigate the dynamics of a charge qubit consisting of two Coulomb-blockaded islands hosting Majorana zero modes. The frequency of the qubit is determined by coherent single-electron tunneling between two islands originating from the hybridization of two Majorana zero modes localized at the junction. We calculate the sequential tunneling current $I$ thr

ChunJun Cao, Brad Lackey

We explicitly construct a class of holographic quantum error correction codes with non-trivial centers in the code subalgebra. Specifically, we use the Bacon-Shor codes and perfect tensors to construct a gauge code (or a stabilizer code with gauge-fixing), which we call the holographic hybrid code. This code admits a local log-depth encoding/decoding circuit

Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao

Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized. This form of collaborative learning exposes new tradeoffs among model convergence speed, model accuracy, balance across clients, and communication cost, with new challenges including: (1) straggler problem, where the clients lag due

Kishan Ramesh Kumar, Artur A. Makhmutov, Christopher J. Spiers, Hadi Hajibeygi

A promising option for storing large-scale quantities of green gases (e.g., hydrogen) is in subsurface rock salt caverns. The mechanical performance of salt caverns utilized for long-term subsurface energy storage plays a significant role in long-term stability and serviceability. However, rock salt undergoes non-linear creep deformation due to long-term loa

Pavao Mardešić, Goran Radunović, Maja Resman

In this paper, we prove that fractal zeta functions of orbits of parabolic germs of diffeomorphisms can be meromorphically extended to the whole complex plane. We describe their set of poles (i.e. their complex dimensions) and their principal parts which can be understood as their fractal footprint. We study the fractal footprint of one orbit of a parabolic

Semyon Litvinov

We prove that the ergodic Ces\' aro averages generated by a positive Dunford-Schwartz operator in a noncommutative space $L^p(\mathcal M,\tau)$, $1<p<\infty$, converge almost uniformly (in Egorov's sense). This problem goes back to the original paper of Yeadon \cite{ye}, where bilaterally almost uniform convergence of these averages was established for $p=1$

Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da

Recent years have brought about a renewed interest in commonsense representation and reasoning in the field of natural language understanding. The development of new commonsense knowledge graphs (CSKG) has been central to these advances as their diverse facts can be used and referenced by machine learning models for tackling new and challenging tasks. At the

Hung Tran, Detang Zhou

In this sequence, we first prove an abstract Morse index theorem in a Hilbert space modeling a variational problem with constraints. Then, our abstract formulation is applied to study several optimization setups including closed CMC hypersurfaces, capillary surfaces in a ball, and critical points of type-II partitioning. In this paper, we study the index and

Hung Tran, Detang Zhou

This is the second paper in our sequence. Here, we apply our abstract Morse index formulation developed in the previous paper to study several optimization set-ups with constraints, including type I or/and type II considerations. A common theme is that critical points belong to the family of capillary surfaces, defined by constant mean curvature and intersec

Daniel Groos, Lars Adde, Ragnhild Støen, Heri Ramampiaro

Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infa

Niall Taggart

By analogy with complex $K$--theory and $K$--theory with reality, there are theories of unitary functor calculus and unitary functor calculus with reality, both of which are generalisations of Weiss&#39; orthogonal calculus. In this paper we show that unitary functor calculus can be completely recovered from the unitary functor calculus with reality, in anal

Vasileios I. Kiosses

A new localization scheme for Klein-Gordon particle states is introduced in the form of general space and time operators. The definition of these operators is achieved by establishing a second quantum field in the momentum space of the standard field we want to localize (here Klein-Gordon field). The motivation for defining a new field in momentum space is a

Ajesh Kumar, Ming Xie, A. H. MacDonald

We show that the insulating states of magic-angle twisted bilayer graphene support a series of collective modes corresponding to local particle-hole excitations on triangular lattice sites. Our theory is based on a continuum model of the magic angle flat bands. When the system is insulating at moiré band filling $ν=-3$, our calculations show that the ground

Tanuman Ghosh, Banibrata Mukhopadhyay

We show the appearance of geometric phase in a Dirac particle traversing in non-relativistic limit in a time-independent gravitational field. This turns out to be similar to the one originally described as a geometric phase in magnetic fields. We explore the geometric phase in the Kerr and Schwarzschild geometries, which have significant astrophysical implic

Régis de la Bretèche, Daniel Fiorilli

We establish unconditional $Ω$-results for all weighted even moments of primes in arithmetic progressions. We also study the moments of these moments and establish lower bounds under GRH. Finally, under GRH and LI we prove an asymptotic for all moments of the associated limiting distribution, which in turn indicates that our unconditional and GRH results are

Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, Rafael S. de Souza

The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions

B. D. E. McNiven, J. P. F. LeBlanc, G. T. Andrews

Room-temperature optical constants of crystalline Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ were determined using data extracted from Brillouin light scattering spectra. Optical extinction coefficient-to-refractive index ratios at a wavelength of 532 nm were obtained from bulk phonon peak linewidth and frequency shift measurements and range from $0.19 \leq 2κ/n \leq 0.2

Ankit Beniwal, Juan Herrero-García, Nicholas Leerdam, Martin White

The Scotogenic Model is one of the most minimal models to account for both neutrino masses and dark matter (DM). In this model, neutrino masses are generated at the one-loop level, and in principle, both the lightest fermion singlet and the lightest neutral component of the scalar doublet can be viable DM candidates. However, the correct DM relic abundance c

Iosif Bena, G. Bruno De Luca, Mariana Graña, Gabriele Lo Monaco

We analyze the stability of four-dimensional de Sitter vacua constructed by compactifying massive Type IIA supergravity in the presence of two O8 sources [1]. When embedded in String Theory the first source has a clear interpretation as an O8$_-$ plane, but the second one could correspond to either an O8$_+$ plane or to an O8$_-$ plane with 16 D8-branes on t

Friedrich Krien, Alexander I. Lichtenstein, Georg Rohringer

We apply the boson exchange parquet solver for dual fermions to the half-filled Hubbard model on a square lattice at small interaction. Our results establish that, in this regime, nonlocal vertex corrections play an important role in the formation of the pseudogap. Namely, in comparison to the simpler ladder approximation, these additional vertex corrections

Zainab Nazari, Michele Cicoli, Katy Clough, Francesco Muia

Using numerical relativity simulations we study the dynamics of pseudo-topological objects called oscillons for a class of models inspired by axion-monodromy. Starting from free field solutions supported by gravitational attractions, we investigate the effect of adding self-interactions, and contrast this with the effect of adding self-interactions whilst re

Ollie Burke, Jonathan R. Gair, Joan Simón, Matthew C. Edwards

We describe a model that generates first order adiabatic EMRI waveforms for quasi-circular equatorial inspirals of compact objects into rapidly rotating (near-extremal) black holes. Using our model, we show that LISA could measure the spin parameter of near-extremal black holes (for $a \gtrsim 0.9999$) with extraordinary precision, $\sim$ 3-4 orders of magni

Sebastian Trujillo-Gomez, J. M. Diederik Kruijssen, Benjamin W. Keller, Marta Reina-Campos

The ultra-diffuse galaxy (UDG) NGC1052-DF2 has a low dark matter content and hosts a very unusual globular cluster (GC) population, with a median luminosity $\sim4$ times higher than in most galaxies and containing about 5~per~cent of the galaxy&#39;s stars. We apply a theoretical model that predicts the initial cluster mass function as a function of the gal

Dave Anderson, Linda Chen, Nicola Tarasca

Motivic Chern and Hirzebruch classes are polynomials with K-theory and homology classes as coefficients, which specialize to Chern-Schwartz-MacPherson classes, K-theory classes, and Cappell-Shaneson L-classes. We provide formulas to compute the motivic Chern and Hirzebruch classes of Grassmannian and vexillary degeneracy loci. We apply our results to obtain

Oleg Evnin, Victor Massart, Kevin Nguyen

A dynamical resolution to the cosmological constant fine-tuning problem has been previously put forward, based on a scalar-tensor gravitational theory possessing de Sitter attractor solutions characterized by a small Hubble expansion rate, irrespective of an initially large vacuum energy. We show that a technically natural subregion of the parameter space yi

LSST Dark Energy Science Collaboration, Bela Abolfathi, David Alonso, Robert Armstrong

We describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) by the LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark of LSST; the DC2 program represent

Martin Kliesch, Ingo Roth

The precise control of complex quantum systems promises numerous technological applications including digital quantum computing. The complexity of such devices renders the certification of their correct functioning a challenge. To address this challenge, numerous methods were developed in the last decade. In this tutorial, we explain prominent protocols for

J. Muir, E. Baxter, V. Miranda, C. Doux

We analyze Dark Energy Survey (DES) data to constrain a cosmological model where a subset of parameters -- focusing on $Ω_m$ -- are split into versions associated with structure growth (e.g. $Ω_m^{\rm grow}$) and expansion history (e.g. $Ω_m^{\rm geo}$). Once the parameters have been specified for the $Λ$CDM cosmological model, which includes general relativ

J. L. Diaz-Cruz, U. J. Saldana-Salazar, K. M. Tame-Narvaez, V. T. Tenorth

Motivated by the fermion mass hierarchy we study the phenomenology of two flavorful two-Higgs-doublet model (2HDM) scenarios. By virtue of the flavor or singular alignment ansatz it is possible to link the mass of a subset of fermions to the vacuum-expectation-value (VEV) of a unique Higgs doublet and to simultaneously avoid flavor-changing-neutral-currents

Matteo Bianconi, Riccardo Buscicchio, Graham P. Smith, Sean L. McGee

We present the direct detection of the splashback feature using the sample of massive galaxy clusters from the Local Cluster Substructure Survey (LoCuSS). This feature is clearly detected (above $5σ$) in the stacked luminosity density profile obtained using the K-band magnitudes of spectroscopically confirmed cluster members. We obtained the best-fit model b

Gian Luigi Granato, Cinthia Ragone-Figueroa, Antonela Taverna, Laura Silva

We present cosmological zoom-in hydro-dynamical simulations for the formation of disc galaxies, implementing dust evolution and dust promoted cooling of hot gas. We couple an improved version of our previous treatment of dust evolution, which adopts the two-size approximation to estimate the grain size distribution, with the MUPPI star formation and feedback

Valeriya Korol, Vasily Belokurov, Christopher J. Moore, Silvia Toonen

White dwarf stars are a well-established tool for studying Galactic stellar populations. Two white dwarfs in a tight binary system offer us an additional messenger - gravitational waves - for exploring the Milky Way and its immediate surroundings. Gravitational waves produced by double white dwarf (DWD) binaries can be detected by the future Laser Interferom

Matteo Baggioli

Numerous experimental and theoretical results in liquids and plasmas suggest the presence of a critical momentum at which the shear diffusion mode collides with a non-hydrodynamic relaxation mode, giving rise to propagating shear waves. This phenomenon, labelled as &#34;k-gap&#34;, could explain the surprising identification of a low-frequency elastic behavi

Giovanni Banelli, Ennio Salvioni, Javi Serra, Tobias Theil

We study the phenomenology of a strongly-interacting top quark at future hadron and lepton colliders, showing that the characteristic four-top contact operators give rise to the most significant effects. We demonstrate the extraordinary potential of a 100 TeV proton-proton collider to directly test such non-standard interactions in four-top production, a pro

Oliver H. E. Philcox, Alejandro Aviles, Elena Massara

We present the one-loop perturbation theory for the power spectrum of the marked density field of matter and biased tracers in real- and redshift-space. The statistic has been shown to yield impressive constraints on cosmological parameters; to exploit this, we require an accurate and computationally inexpensive theoretical model. Comparison with $N$-body si

Hai Tao Li, Ivan Vitev

Jet production and jet substructure in reactions with nuclei at future electron-ion colliders will play a preeminent role in the exploration of nuclear structure and the evolution of parton showers in strongly-interacting matter. In the framework of soft-collinear effective theory, generalized to include in-medium interactions, we present the first theoretic

Anand Bhattad, David A. Forsyth

We show how to insert an object from one image to another and get realistic results in the hard case, where the shading of the inserted object clashes with the shading of the scene. Rendering objects using an illumination model of the scene doesn&#39;t work, because doing so requires a geometric and material model of the object, which is hard to recover from

Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula

Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future. However, simultaneous incorporation of past and future contexts using generative language models (LMs) can be challenging, as they are traine

Chaosheng Dong, Yijia Wang, Bo Zeng

We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and possibly carry measurement noise or are generated with the bounded rationality of decision makers. In this paper, we propose a general online

Tal Reiss, Niv Cohen, Liron Bergman, Yedid Hoshen

Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pretrained deep features, has been mostly overlooked. In this paper, we first empirically establish the perha

Parisa Rahmani, Fernando Peruani, Pawel Romanczuk

Flocking models with metric and topological interactions are supposed to exhibit distinct features, as for instance the presence and absence of moving polar bands. On the other hand, quenched disorder (spatial heterogeneities) has been shown to dramatically affect large-scale properties of active systems with metric interactions, while the impact of quenched

Zihan Zhang, Simon S. Du, Xiangyang Ji

We study the reward-free reinforcement learning framework, which is particularly suitable for batch reinforcement learning and scenarios where one needs policies for multiple reward functions. This framework has two phases. In the exploration phase, the agent collects trajectories by interacting with the environment without using any reward signal. In the pl

Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan

High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying molecular interactions across different data types helps reveal signal transduction mechanisms across different classes of molecules. In this paper, we develop a novel Bayesian rep

Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer

Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random memory accesses needed to lookup the embedding tables. The inference is also heavily constrained in terms of latency because producing a recommendation for a user must be done in

Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford

We propose and analyze algorithms for distributionally robust optimization of convex losses with conditional value at risk (CVaR) and $χ^2$ divergence uncertainty sets. We prove that our algorithms require a number of gradient evaluations independent of training set size and number of parameters, making them suitable for large-scale applications. For $χ^2$ u

Christian Ebenbauer, Fabian Pfitz, Shuyou Yu

A receding horizon learning scheme is proposed to transfer the state of a discrete-time dynamical control system to zero without the need of a system model. Global state convergence to zero is proved for the class of stabilizable and detectable linear time-invariant systems, assuming that only input and output data is available and an upper bound of the stat

Cristina Ana-Maria Anghel

In this paper we prove a unified model for $U_q(sl(2))$ quantum invariants through intersections of embedded Lagrangians in configuration spaces. More specifically, we construct a {\em state sum of Lagrangian intersections in the configuration space in the punctured disc}, which is a polynomial in three variables. It {\em recovers the coloured Jones polynomi

Rachid Guerraoui, Arsany Guirguis, Jérémy Max Plassmann, Anton Alexandre Ragot

We present Garfield, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine-resilient. Garfield relies on a novel object-oriented design, reducing the coding effort, and addressing the vulnerability of the shared-graph architecture followed by classica

Jan Brüdigam, Zachary Manchester

The linear-quadratic regulator (LQR) is an efficient control method for linear and linearized systems. Typically, LQR is implemented in minimal coordinates (also called generalized or &#34;joint&#34; coordinates). However, other coordinates are possible and recent research suggests that there may be numerical and control-theoretic advantages when using highe

Milton Aguilar, Nahuel Freitas, Juan Pablo Paz

We show that in a linear quantum machine, a driven quantum system that evolves while coupled with thermal reservoirs, entanglement between the reservoir modes is unavoidably generated. This phenomenon, which occurs at sufficiently low temperatures and is at the heart of the third law of thermodynamics, is a consequence of a simple process: the transformation

Adrien Dubouloz, Takashi Kishimoto, Karol Palka

We describe a method to construct completions of affine spaces into total spaces of $\mathbb{Q}$-factorial terminal Mori fiber spaces over the projective line. As an application we provide families of examples with non-rational, birationally rigid and non-stably rational general fibers.

Dorin Bucur, Alessandro Giacomini, Mickaël Nahon

We introduce a new geometric-analytic functional that we analyse in the context of free discontinuity problems. Its main feature is that the geometric term (the length of the jump set) appears with negative sign. This is motivated by searching quantitative inequalities for best constants of Sobolev-Poincar\'e inequalities with trace terms in $\mathbb{R}^n$ w

Galina Zverkina

It is more important to estimate the rate of convergence to a stationary distribution rather than only to prove the existence one in many applied problems of reliability and queuing theory. This can be done via standard methods, but only under assumptions about an exponential distribution of service time, independent intervals between recovery times, etc. Re

Yulia Karpeshina, Leonid Parnovski, Roman Shterenberg

We consider Schr\"odinger operators $H=-\Delta+V({\mathbf x})$ in ${\mathbb R}^d$, $d\geq2$, with quasi-periodic potentials $V({\mathbf x})$. We prove that the absolutely continuous spectrum of a generic $H$ contains a semi-axis $[\lambda_*,+\infty)$. We also construct a family of eigenfunctions of the absolutely continuous spectrum; these eigenfunctions are

Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang

Many large-scale applications amount to finding relevant results from an enormous output space of potential candidates. For example, finding the best matching product from a large catalog or suggesting related search phrases on a search engine. The size of the output space for these problems can range from millions to billions, and can even be infinite in so

Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan

Data management is becoming increasingly important in dealing with the large amounts of data produced by large-scale scientific simulations and instruments. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale, but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+

Elmer Guardado-Sanchez, Benjamin M. Spar, Peter Schauss, Ron Belyansky

We induce strong non-local interactions in a 2D Fermi gas in an optical lattice using Rydberg dressing. The system is approximately described by a $t-V$ model on a square lattice where the fermions experience isotropic nearest-neighbor interactions and are free to hop only along one direction. We measure the interactions using many-body Ramsey interferometry

Yael Hillman, Amit Kashi

We use a combined binary evolution code including dynamical effects to study nova eruptions in a symbiotic system. Following the evolution, over $\sim10^5$ years, of multiple consecutive nova eruptions on the surface of a $1.25M_\odot$ white dwarf (WD) accretor, we present a comparison between simulations of two types of systems. The first is the common, wel

Matthew Ceko, Silvia M. C. Pagani, Rob Tijdeman

The goal of discrete tomography is to reconstruct an unknown function $f$ via a given set of line sums. In addition to requiring accurate reconstructions, it is favourable to be able to perform the task in a timely manner. This is complicated by the presence of ghosts, which allow many solutions to exist in general. In this paper we consider the case of a fu

Zhan Gao, Fernando Gama, Alejandro Ribeiro

Spherical convolutional neural networks (Spherical CNNs) learn nonlinear representations from 3D data by exploiting the data structure and have shown promising performance in shape analysis, object classification, and planning among others. This paper investigates the properties that Spherical CNNs exhibit as they pertain to the rotational structure inherent

Bruno Colbois, Luigi Provenzano

In this note we present upper bounds for the variational eigenvalues of the $p$-Laplacian on smooth domains of complete $n$-dimensional Riemannian manifolds and Neumann boundary conditions, and on compact (boundaryless) Riemannian manifolds. In particular, we provide upper bounds in the conformal class of a given manifold $(M,g)$ for $1<p\leq n$, and upper b

Ze Long Liu, Maximilian Stahlhofen

We calculate the three-loop soft function for the production of an electroweak boson (Higgs, $γ$, $W^\pm$, $Z$) with large transverse momentum at a hadron collider. It is the first time a soft function for a three-parton process is computed at next-to-next-to-next-to-leading order (N$^3$LO). As a technical novelty, we perform the calculation in terms of forw

Alessandro Savo

We study and classify smooth bounded domains in an analytic Riemannian manifold which are critical for the heat content at all times t>0. We do that by first computing the first variation of the heat content, and then showing that a domain is critical if and only if it has the so-called constant flow property, so that we can use a previous classification res

Mingda Chen, Sam Wiseman, Kevin Gimpel

Most prior work on exemplar-based syntactically controlled paraphrase generation relies on automatically-constructed large-scale paraphrase datasets, which are costly to create. We sidestep this prerequisite by adapting models from prior work to be able to learn solely from bilingual text (bitext). Despite only using bitext for training, and in near zero-sho

Anubhav Chaturvedi, Máté Farkas, Victoria J Wright

The predictions of quantum theory resist generalised noncontextual explanations. In addition to the foundational relevance of this fact, the particular extent to which quantum theory violates noncontextuality limits available quantum advantage in communication and information processing. In the first part of this work, we formally define contextuality scenar

Minyi Dai, Mehmet F. Demirel, Yingyu Liang, Jia-Mian Hu

Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embeddin

Zhi-Wei Sun

In this paper we mainly study sums of four rational squares with certain restrictions. Let $\mathbb Q_{\ge0}$ be the set of nonnegative rational numbers. We establish the following four-square theorem for rational numbers: For any $a,b,c,d\in\mathbb Q_{\ge0}$, each $r\in\mathbb Q_{\ge0}$ can be written as $x^2+y^2+z^2+w^2$ with $x,y,z,w\in\mathbb Q_{\ge0}$ s

Matthias Linden, Jonas Dehning, Sebastian B. Mohr, Jan Mohring

A second wave of SARS-CoV-2 is unfolding in dozens of countries. However, this second wave manifests itself strongly in new reported cases, but less in death counts compared to the first wave. Over the past three months in Germany, the reported cases increased by a factor five or more, whereas the death counts hardly grew. This discrepancy fueled speculation

Rustum Choksi, Irene Fonseca, Jessica Lin, Raghavendra Venkatraman

This paper establishes bounds on the homogenized surface tension for a heterogeneous Allen-Cahn energy functional in a periodic medium. The approach is based on relating the homogenized energy to a purely geometric variational problem involving the large scale behaviour of the signed distance function to a hyperplane in periodic media. Motivated by this, a h

Costante Bellettini, Neshan Wickramasekera

We prove that for any given compact Riemannian manifold $N$ of dimension $n+1 \geq 3$ and any non-negative Lipschitz function $g$ on $N$, there exists a quasi-embedded, boundaryless hypersurface $M \subset N,$ of class $C^{2, α}$ for any $α\in (0,1),$ such that $M$ is the image of a two-sided immersion whose mean curvature is given by $gν$ for an appropriate